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21 Commits

Author SHA1 Message Date
Aaron Orenstein
805c4b597a PEP585 update - torch/_higher_order_ops torch/_subclasses torch/backends torch/compiler torch/cuda torch/masked torch/mtia torch/nested (#145202) 
See #145101 for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145202
Approved by: https://github.com/bobrenjc93
 2025-01-20 22:37:26 +00:00
Xuehai Pan
758a0a88a2 [BE][Easy] enable ruff rule PIE790: unnecessary pass statement (#133200) 
This PR removes unnecessary `pass` statement. This is semanticly safe because the bytecode for the Python code does not change.

Note that if there is a docstring in the function, a empty function does not need a `pass` statement as placeholder.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/133200
Approved by: https://github.com/malfet, https://github.com/eqy, https://github.com/kit1980
 2024-08-15 15:50:19 +00:00
Xuehai Pan
f3fce597e9 [BE][Easy][17/19] enforce style for empty lines in import segments in torch/[a-c]*/ and torch/[e-n]*/ (#129769) 
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter.

You can review these PRs via:

```bash
git diff --ignore-all-space --ignore-blank-lines HEAD~1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129769
Approved by: https://github.com/ezyang
 2024-08-04 10:24:09 +00:00
PyTorch MergeBot
609447a626 Revert "[BE] typing for decorators - _jit_internal (#131573)" 
This reverts commit f0f20f7e97.

Reverted https://github.com/pytorch/pytorch/pull/131573 on behalf of https://github.com/clee2000 due to breaking lint internally D60265575 ([comment](https://github.com/pytorch/pytorch/pull/131572#issuecomment-2254328359))
 2024-07-28 03:29:32 +00:00
Aaron Orenstein
f0f20f7e97 [BE] typing for decorators - _jit_internal (#131573) 
See #131429

Pull Request resolved: https://github.com/pytorch/pytorch/pull/131573
Approved by: https://github.com/oulgen, https://github.com/zou3519
ghstack dependencies: #131568, #131569, #131570, #131571, #131572
 2024-07-25 22:24:19 +00:00
Aaron Orenstein
5a0068cc69 [BE] mypy: disallow untyped decorators (#131428) 
Untyped decorators strip the types from their decorated function so even if the underlying function is fully typed then callers to it don't get any benefit from type annotations.

Step 1 - Enable the error and override in all the offending files.

#131429

Pull Request resolved: https://github.com/pytorch/pytorch/pull/131428
Approved by: https://github.com/justinchuby, https://github.com/oulgen
 2024-07-23 21:50:55 +00:00
Aaron Orenstein
62bcdc0ac9 Flip default value for mypy disallow_untyped_defs [4/11] (#127841) 
See #127836 for details.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127841
Approved by: https://github.com/oulgen
 2024-06-08 18:36:48 +00:00
Aaron Gokaslan
8cad88e1f3 [BE]: Improve exception typing. Remove NOQAs (#125535) 
Improve some exception typing

Pull Request resolved: https://github.com/pytorch/pytorch/pull/125535
Approved by: https://github.com/albanD
 2024-05-08 14:07:13 +00:00
Aaron Gokaslan
c5fafe9f48 [BE]: TRY002 - Ban raising vanilla exceptions (#124570) 
Adds a ruff lint rule to ban raising raw exceptions. Most of these should at the very least be runtime exception, value errors, type errors or some other errors. There are hundreds of instance of these bad exception types already in the codebase, so I have noqa'd most of them. Hopefully this error code will get commiters to rethink what exception type they should raise when they submit a PR.

I also encourage people to gradually go and fix all the existing noqas that have been added so they can be removed overtime and our exception typing can be improved.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124570
Approved by: https://github.com/ezyang
 2024-04-21 22:26:40 +00:00
NVS Abhilash
eb5487361d docs: fix docstring errors in quantized modules and others (#112695) 
Fixes #112632

Before: 171
```
torch/backends/_nnapi/prepare.py:24 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/_nnapi/prepare.py:46 in public method `init`:
        D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:60 in public method `forward`:
        D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:94 in public function `convert_model_to_nnapi`:
        D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:153 in public function `process_for_nnapi`:
        D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:177 in private nested class `ShapeComputeModule`:
        D400: First line should end with a period (not 'n')
torch/backends/_nnapi/serializer.py:19 in public class `NNAPI_OperandCode`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:35 in public class `NNAPI_OperationCode`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:133 in public class `NNAPI_FuseCode`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:140 in public class `OperandValueSourceType`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:150 in public class `TorchScalarTypes`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:154 in public function `approx_equal`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:158 in public function `tensor_size`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:172 in public function `change_element`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:194 in public class `DimOrder`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:225 in public method `use_nchw`:
        D102: Missing docstring in public method
torch/backends/_nnapi/serializer.py:233 in public function `broadcast_shapes`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:260 in public function `get_conv_pool_shape`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:284 in public function `fix_shape`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:301 in public function `reverse_map_dim`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:312 in public function `flex_name`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:1337 in private method `_do_add_binary`:
        D400: First line should end with a period (not 's')
torch/backends/_nnapi/serializer.py:1337 in private method `_do_add_binary`:
        D401: First line should be in imperative mood; try rephrasing (found 'Helper')
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
        D202: No blank lines allowed after function docstring (found 1)
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
        D205: 1 blank line required between summary line and description (found 0)
torch/backends/_nnapi/serializer.py:2180 in public function `serialize_model`:
        D400: First line should end with a period (not ':')
torch/backends/cuda/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/cuda/__init__.py:30 in public function `is_built`:
        D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:30 in public function `is_built`:
        D209: Multi-line docstring closing quotes should be on a separate line
torch/backends/cuda/__init__.py:30 in public function `is_built`:
        D400: First line should end with a period (not 's')
torch/backends/cuda/__init__.py:30 in public function `is_built`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cuda/__init__.py:37 in public class `cuFFTPlanCacheAttrContextProp`:
        D101: Missing docstring in public class
torch/backends/cuda/__init__.py:40 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:44 in public method `__get__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:47 in public method `__set__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:54 in public class `cuFFTPlanCache`:
        D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:54 in public class `cuFFTPlanCache`:
        D400: First line should end with a period (not 'e')
torch/backends/cuda/__init__.py:60 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:73 in public method `clear`:
        D102: Missing docstring in public method
torch/backends/cuda/__init__.py:78 in public class `cuFFTPlanCacheManager`:
        D205: 1 blank line required between summary line and description (found 0)
torch/backends/cuda/__init__.py:78 in public class `cuFFTPlanCacheManager`:
        D400: First line should end with a period (not ',')
torch/backends/cuda/__init__.py:89 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:93 in public method `__getitem__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:106 in public method `__getattr__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:109 in public method `__setattr__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:116 in public class `cuBLASModule`:
        D101: Missing docstring in public class
torch/backends/cuda/__init__.py:117 in public method `__getattr__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:126 in public method `__setattr__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:147 in public function `preferred_linalg_library`:
        D202: No blank lines allowed after function docstring (found 1)
torch/backends/cuda/__init__.py:204 in public class `SDPBackend`:
        D204: 1 blank line required after class docstring (found 0)
torch/backends/cudnn/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/cudnn/__init__.py:81 in public function `version`:
        D400: First line should end with a period (not 'N')
torch/backends/cudnn/__init__.py:81 in public function `version`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cudnn/__init__.py:95 in public function `is_available`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/cudnn/__init__.py:99 in public function `is_acceptable`:
        D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:122 in public function `set_flags`:
        D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:150 in public function `flags`:
        D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:174 in public class `CudnnModule`:
        D101: Missing docstring in public class
torch/backends/cudnn/__init__.py:175 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/mkl/__init__.py:5 in public function `is_available`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/mkl/__init__.py:14 in public class `verbose`:
        D205: 1 blank line required between summary line and description (found 0)
torch/backends/mkl/__init__.py:14 in public class `verbose`:
        D400: First line should end with a period (not 'y')
torch/backends/mkl/__init__.py:41 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:44 in public method `__enter__`:
        D105: Missing docstring in magic method
torch/backends/mkl/__init__.py:53 in public method `__exit__`:
        D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/mkldnn/__init__.py:9 in public function `is_available`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/backends/mkldnn/__init__.py:19 in public class `verbose`:
        D205: 1 blank line required between summary line and description (found 0)
torch/backends/mkldnn/__init__.py:19 in public class `verbose`:
        D400: First line should end with a period (not 'y')
torch/backends/mkldnn/__init__.py:47 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/mkldnn/__init__.py:50 in public method `__enter__`:
        D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:59 in public method `__exit__`:
        D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:64 in public function `set_flags`:
        D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:71 in public function `flags`:
        D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:81 in public class `MkldnnModule`:
        D101: Missing docstring in public class
torch/backends/mkldnn/__init__.py:82 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/openmp/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/openmp/__init__.py:5 in public function `is_available`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/intrinsic/qat/modules/conv_fused.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/intrinsic/qat/modules/linear_fused.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/intrinsic/qat/modules/linear_relu.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/qat/__init__.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/qat/dynamic/__init__.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/qat/dynamic/modules/linear.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/qat/modules/__init__.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/qat/modules/conv.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/qat/modules/embedding_ops.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/qat/modules/linear.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantizable/modules/activation.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantizable/modules/rnn.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/__init__.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/conv.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/linear.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/rnn.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/sparse.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/_reference/modules/utils.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/__init__.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/conv.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/linear.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/dynamic/modules/rnn.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/functional.py:1 at module level:
        D400: First line should end with a period (not 'l')
torch/nn/quantized/modules/__init__.py:1 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/activation.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/batchnorm.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/conv.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/dropout.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/embedding_ops.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/functional_modules.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/linear.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/normalization.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/rnn.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/quantized/modules/utils.py:2 at module level:
        D400: First line should end with a period (not 's')
torch/nn/utils/_expanded_weights/conv_utils.py:13 in public function `conv_picker`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:23 in public function `conv_args_and_kwargs`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:31 in public function `conv_normalizer`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:35 in public function `conv_input_for_string_padding`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:43 in public function `int_padding_for_string_padding`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:59 in public function `conv_padding_for_same`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:66 in public function `conv_backward`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:131 in public function `conv_unfold_weight_grad_sample`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:166 in public function `conv_group_weight_grad_sample`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
        D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
        D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/conv_utils.py:189 in public function `unfold3d`:
        D401: First line should be in imperative mood (perhaps 'Extract', not 'Extracts')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:6 in public function `is_batch_first`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
        D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
        D300: Use """triple double quotes""" (found '''-quotes)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:19 in public function `standard_kwargs`:
        D400: First line should end with a period (not 'e')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
        D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
        D300: Use """triple double quotes""" (found '''-quotes)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:28 in public function `forward_helper`:
        D400: First line should end with a period (not ')')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:84 in public function `maybe_scale_by_batch_size`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:90 in public function `set_grad_sample_if_exists`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:108 in public function `unpack_expanded_weight_or_tensor`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
        D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
        D400: First line should end with a period (not 't')
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:123 in public function `sum_over_all_but_batch_and_last_n`:
        D401: First line should be in imperative mood (perhaps 'Calculate', not 'Calculates')
torch/nn/utils/convert_parameters.py:1 at module level:
        D100: Missing docstring in public module
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
        D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
        D205: 1 blank line required between summary line and description (found 0)
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
        D400: First line should end with a period (not 'd')
torch/nn/utils/convert_parameters.py:57 in private function `_check_param_device`:
        D401: First line should be in imperative mood; try rephrasing (found 'This')
torch/nn/utils/rnn.py:1 at module level:
        D100: Missing docstring in public module
torch/nn/utils/rnn.py:28 in public class `PackedSequence`:
        D204: 1 blank line required after class docstring (found 0)
torch/nn/utils/rnn.py:63 in public method `__new__`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:73 in public method `pin_memory`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:80 in public method `cuda`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:87 in public method `cpu`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:94 in public method `double`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:97 in public method `float`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:100 in public method `half`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:103 in public method `long`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:106 in public method `int`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:109 in public method `short`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:112 in public method `char`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:115 in public method `byte`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:119 in public method `to`:
        D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:119 in public method `to`:
        D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs')
torch/nn/utils/rnn.py:146 in public method `is_cuda`:
        D400: First line should end with a period (not 'u')
torch/nn/utils/rnn.py:150 in public method `is_pinned`:
        D400: First line should end with a period (not 'y')
torch/nn/utils/rnn.py:150 in public method `is_pinned`:
        D401: First line should be in imperative mood (perhaps 'Return', not 'Returns')
torch/nn/utils/rnn.py:198 in public function `invert_permutation`:
        D103: Missing docstring in public function
torch/nn/utils/rnn.py:274 in public function `pad_packed_sequence`:
        D401: First line should be in imperative mood (perhaps 'Pad', not 'Pads')
torch/nn/utils/rnn.py:347 in public function `pad_sequence`:
        D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:347 in public function `pad_sequence`:
        D400: First line should end with a period (not '`')
torch/nn/utils/rnn.py:408 in public function `unpad_sequence`:
        D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:408 in public function `unpad_sequence`:
        D400: First line should end with a period (not 's')
torch/nn/utils/rnn.py:454 in public function `pack_sequence`:
        D400: First line should end with a period (not 's')
torch/nn/utils/rnn.py:490 in public function `unpack_sequence`:
        D202: No blank lines allowed after function docstring (found 1)
torch/nn/utils/rnn.py:490 in public function `unpack_sequence`:
        D400: First line should end with a period (not 's')
171
```

After: 81
```
torch/backends/_nnapi/prepare.py:24 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/_nnapi/prepare.py:46 in public method `init`:
        D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:60 in public method `forward`:
        D102: Missing docstring in public method
torch/backends/_nnapi/prepare.py:94 in public function `convert_model_to_nnapi`:
        D103: Missing docstring in public function
torch/backends/_nnapi/prepare.py:153 in public function `process_for_nnapi`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:19 in public class `NNAPI_OperandCode`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:35 in public class `NNAPI_OperationCode`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:133 in public class `NNAPI_FuseCode`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:140 in public class `OperandValueSourceType`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:150 in public class `TorchScalarTypes`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:154 in public function `approx_equal`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:158 in public function `tensor_size`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:172 in public function `change_element`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:194 in public class `DimOrder`:
        D101: Missing docstring in public class
torch/backends/_nnapi/serializer.py:225 in public method `use_nchw`:
        D102: Missing docstring in public method
torch/backends/_nnapi/serializer.py:233 in public function `broadcast_shapes`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:260 in public function `get_conv_pool_shape`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:284 in public function `fix_shape`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:301 in public function `reverse_map_dim`:
        D103: Missing docstring in public function
torch/backends/_nnapi/serializer.py:312 in public function `flex_name`:
        D103: Missing docstring in public function
torch/backends/cuda/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/cuda/__init__.py:39 in public class `cuFFTPlanCacheAttrContextProp`:
        D101: Missing docstring in public class
torch/backends/cuda/__init__.py:42 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:46 in public method `__get__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:49 in public method `__set__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:63 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:76 in public method `clear`:
        D102: Missing docstring in public method
torch/backends/cuda/__init__.py:91 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/cuda/__init__.py:95 in public method `__getitem__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:108 in public method `__getattr__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:111 in public method `__setattr__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:118 in public class `cuBLASModule`:
        D101: Missing docstring in public class
torch/backends/cuda/__init__.py:119 in public method `__getattr__`:
        D105: Missing docstring in magic method
torch/backends/cuda/__init__.py:128 in public method `__setattr__`:
        D105: Missing docstring in magic method
torch/backends/cudnn/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/cudnn/__init__.py:99 in public function `is_acceptable`:
        D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:122 in public function `set_flags`:
        D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:150 in public function `flags`:
        D103: Missing docstring in public function
torch/backends/cudnn/__init__.py:174 in public class `CudnnModule`:
        D101: Missing docstring in public class
torch/backends/cudnn/__init__.py:175 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/mkl/__init__.py:42 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/mkl/__init__.py:45 in public method `__enter__`:
        D105: Missing docstring in magic method
torch/backends/mkl/__init__.py:54 in public method `__exit__`:
        D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/backends/mkldnn/__init__.py:48 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/mkldnn/__init__.py:51 in public method `__enter__`:
        D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:60 in public method `__exit__`:
        D105: Missing docstring in magic method
torch/backends/mkldnn/__init__.py:65 in public function `set_flags`:
        D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:72 in public function `flags`:
        D103: Missing docstring in public function
torch/backends/mkldnn/__init__.py:82 in public class `MkldnnModule`:
        D101: Missing docstring in public class
torch/backends/mkldnn/__init__.py:83 in public method `__init__`:
        D107: Missing docstring in __init__
torch/backends/openmp/__init__.py:1 at module level:
        D104: Missing docstring in public package
torch/nn/utils/_expanded_weights/conv_utils.py:13 in public function `conv_picker`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:23 in public function `conv_args_and_kwargs`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:31 in public function `conv_normalizer`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:35 in public function `conv_input_for_string_padding`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:43 in public function `int_padding_for_string_padding`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:59 in public function `conv_padding_for_same`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:66 in public function `conv_backward`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:131 in public function `conv_unfold_weight_grad_sample`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/conv_utils.py:166 in public function `conv_group_weight_grad_sample`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:6 in public function `is_batch_first`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:87 in public function `maybe_scale_by_batch_size`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:93 in public function `set_grad_sample_if_exists`:
        D103: Missing docstring in public function
torch/nn/utils/_expanded_weights/expanded_weights_utils.py:111 in public function `unpack_expanded_weight_or_tensor`:
        D103: Missing docstring in public function
torch/nn/utils/convert_parameters.py:1 at module level:
        D100: Missing docstring in public module
torch/nn/utils/rnn.py:1 at module level:
        D100: Missing docstring in public module
torch/nn/utils/rnn.py:64 in public method `__new__`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:74 in public method `pin_memory`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:81 in public method `cuda`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:88 in public method `cpu`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:95 in public method `double`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:98 in public method `float`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:101 in public method `half`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:104 in public method `long`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:107 in public method `int`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:110 in public method `short`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:113 in public method `char`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:116 in public method `byte`:
        D102: Missing docstring in public method
torch/nn/utils/rnn.py:198 in public function `invert_permutation`:
        D103: Missing docstring in public function
81
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/112695
Approved by: https://github.com/mikaylagawarecki
 2023-11-07 23:52:16 +00:00
Edward Z. Yang
3bf922a6ce Apply UFMT to low traffic torch modules (#106249) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106249
Approved by: https://github.com/Skylion007
 2023-07-29 23:37:30 +00:00
Akshit Khurana
bb3e1f30a8 [Pytorch NNAPI] Add compilation_preference & relax_f32_to_f16 APIs (#78758) 
Summary:
compilation_preference is one of:

ANEURALNETWORKS_PREFER_LOW_POWER = 0
ANEURALNETWORKS_PREFER_FAST_SINGLE_ANSWER = 1
ANEURALNETWORKS_PREFER_SUSTAINED_SPEED = 2

relax_f32_to_f16 calls Model_relaxComputationFloat32toFloat16

Test Plan:
Tested on device with nnapi models

* Works with existing exported models
* Works with new exported models with options

Differential Revision: D36433236

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78758
Approved by: https://github.com/kimishpatel
 2022-06-06 20:57:34 +00:00
Akshit Khurana
44283c2766 NNAPI: Add qint16 support via int16 (#70621) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70621

Pytorch doesn't have support for qint16 yet. Add an option to handle qint16 via int16 & qint32 data types.

* For qint16 tensors in NNAPI, the user sends a qint32 tensor. We convert the qint32 to int16 for the converter and set the zero point and scale for nnapi
    * inputs to the model have to have fixed scale and zero point and are only supported for testing
* Added a flag use_int16_for_qint16 which will be used maintain backwards compatibility in the converter when true qint16 is supported in PyTorch
ghstack-source-id: 146507483

Test Plan: pytest test/test_nnapi.py

Reviewed By: dreiss

Differential Revision: D33285124

fbshipit-source-id: b6376fa1bb18a0b9f6a18c545f600222b650cb66
 2022-01-04 23:12:38 -08:00
Akshit Khurana
1150046d29 NNAPI: Add runtime flexible shapes & return shapes (#70334) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/70334

* Use 0 for load time flexible shapes
* -1 for runtime flexible shapes
* NNAPI needs return shapes for flexible outputs

Test Plan: Tested via upcoming ops

Reviewed By: dreiss

Differential Revision: D33237922

fbshipit-source-id: 50afdd8e3c6401dfb79b4bc09513c9882a09e5d5
 2022-01-04 08:37:09 -08:00
Amy He
73f1e2d1dc [8/N] Nnapi backend delegation preprocess: New refactored design (#62225) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62225

Rewrote the preprocess function for Android NNAPI delegate.
Previously, `preprocess()` called `convert_model_to_nnapi()` using Pybind and returned a NnapiModule that is serialized for mobile. Now, `preprocess()` calls a sub-function of `convert_model_to_nnapi()` and returns several preprocessed items (that were previously components of NnapiModule).

Dictionary returned contains:
   "shape_compute_module": torch::jit::Module,
   "ser_model": torch::Tensor,
   "weights": List[torch.Tensor],
   "inp_mem_fmts": List[int],
   "out_mem_fmts": List[int]

**Purpose and Future:**
The purpose of these changes are to move more implementation from bytecode and Torchscript to the delegate API, since bytecode is less efficient.
Now, only the shape computation uses bytecode. In the future, shape computation will be moved out of Torchscript as well.

**nnapi_backend_preprocess.cpp:** preprocess implementation
**prepare.py**: refactored a portion of `convert_model_to_nnapi()` to `process_for_nnapi()`, so preprocess can get components of NnapiModule

**Test:**
Ran `python test/test_jit.py TestNnapiBackend` and `python test/test_nnapi.py` on OSS successfully
ghstack-source-id: 134444190

Test Plan: Ran `python test/test_jit.py TestNnapiBackend` and `python test/test_nnapi.py` on OSS successfully

Reviewed By: raziel

Differential Revision: D29922279

fbshipit-source-id: cadcf8908d8a745dc7abbe286e97d6ead937d4ab
 2021-07-27 18:52:48 -07:00
Akshit Khurana
a3670ba377 Add option to specify custom NNAPI serializer (#61025) 
Summary:
To add serializer for custom ops we can subclass default serializer
and update ADDER_MAP

Pull Request resolved: https://github.com/pytorch/pytorch/pull/61025

Test Plan:
* pytest test/test_nnapi.py::TestNNAPI for current serializer
* Custom serializers to be tested with custom ops

Imported from OSS

Reviewed By: anshuljain1

Differential Revision: D29480745

fbshipit-source-id: 37e3f8de3c97f6c8a486f9879ce11430ea89af34
 2021-07-09 15:27:10 -07:00
Guilherme Leobas
e7c79cb158 Add type annotations to nnapi (#48142) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/48141

~Mypy is complaining about a missing arg in a function call.~
```bash
torch/backends/_nnapi/serializer.py:806: error: Too few arguments for "_do_add_binary"  [call-arg]
Found 1 error in 1 file (checked 1140 source files)
```

9392137dbe/torch/backends/_nnapi/serializer.py (L804-L806)

~dreiss, would you mind take a look when you have some cycles to spare and see what would be the appropriated value for `fuse_code` here? Thanks :)~

Edit: https://github.com/pytorch/pytorch/issues/48925 got merged a couple of days ago. The blocking part is now unblocked, and I just pushed the changes to make mypy happy again. This PR is ready for review.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/48142

Reviewed By: ezyang

Differential Revision: D28006249

Pulled By: walterddr

fbshipit-source-id: 5e43eeba7143512a549efaad31541f86718add7c
 2021-04-26 19:08:07 -07:00
David Reiss
da7a27b847 [NNAPI] Initial flexible size support (#54701) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54701

We need NNAPI models to support inputs (and, by extension, intermediate
values and outputs) whose shape is only determined at load time.  For
example, a vision models input shape might be dependent on the aspect
ratio of the device camera.  While NNAPI has full support for variable
shapes (by setting components of the operand shape to 0), the guidance
we have received is that vendor-provided drivers for real hardware are
not able to support this efficiently.  Therefore, we take a hybrid
approach where shapes are calculated at model load time to
semi-dynamically construct our NNAPI model.  While this doesn't let us
have truly dynamic input shapes, it does allow us to ensure that the
vendor driver only sees fixed shapes, so we get maximum performance.

In this initial commit, only PReLU supports dynamic shapes.  Additional
operators will be converted in separate diffs.

- In order to convert a flexible-shape model, the user supplies inputs
  with shapes containing dimensions of size 0 for the flexible
  dimensions.
- During conversion, we generate code to compute the shapes of all
  intermediates and outputs as a function of the input shapes.
- We no longer run the input model to produce the output templates.
  Instead, we generate code to return properly-sized templates, given
  the input shapes.
- All of this generated code goes into a "ShapeComputeModule" that is
  used by the NnapiModule during initialization.
- The ShapeComputeModule mutates the serialized model to fill in the
  computed sizes for each operand.  This requires us to change the dtype
  for the serialized model to int32, but this should be fine because
  everything in it is already 4-byte aligned.
- NnapiInitWrapper no longer exists.  Instead, initialization is
  performed on the first run, based on the real arguments.  We plan to
  provide an API for doing eager initialization.
- Unit test updated to allow separate arguments to be given for trace,
  conversion, and inference.  A flexible-shape test case was added for
  PReLU.

Test Plan: Unit test

Reviewed By: axitkhurana

Differential Revision: D27536796

Pulled By: dreiss

fbshipit-source-id: 105585f247987b1e6ec6946a6fe44401237cb0a0
 2021-04-06 13:49:43 -07:00
David Reiss
1f1d26137b [NNAPI] Use code generation to better support list input/output (#54697) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54697

Previously, models being converted to NNAPI were expected to take inputs
as separate arguments, but the generated NNAPI model could only take
multiple inputs as a list.  Now the generated model always takes inputs
(single or multiple) as separate tensor arguments.

Previously, models being converted to NNAPI were expected to return
outputs as a single tensor or tuple of tensors, but the generated NNAPI
model would return multiple outputs as a list. Now the generated model
returns a tuple as well (or single tensor).

Internally, we decied what output format to use (single tensor or tuple)
based on the conversion process, rather than by running the model.

Test Plan: Unit test

Reviewed By: axitkhurana

Differential Revision: D27536790

Pulled By: dreiss

fbshipit-source-id: c0f93c85d450757e568985947cc2f32043795859
 2021-04-06 13:49:33 -07:00
David Reiss
1be909f074 [NNAPI] Fix models with no weights (#47517) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47517

While we're unlikely to see this in practice, it comes up in unit tests.
This type annotation is necessary for `torch.jit.script` to figure out
the type of the list if it is empty.

Test Plan: Unit tests in a later diff.

Reviewed By: axitkhurana

Differential Revision: D25317937

Pulled By: dreiss

fbshipit-source-id: de8b6665c6fcd3cd2b39e3c696a39336c064e4c1
 2021-04-06 13:49:06 -07:00
David Reiss
9a9383ef2e PyTorch NNAPI integration prototype (#46780) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46780

This is in prototype status, but pretty functional.  There are two major
parts.

- Model converter.  This is a pure Python component that consumes a
  model in TorchScript format, converts the operations into NNAPI
  semantics, and serializes the model in a custom format.  It then wraps
  the result in a new TorchScript model that can invoke NNAPI under the
  hood.
- Runtime.  This is a TorchBind object that deserializes the model and
  sends the result to NNAPI.  This is fairly simple since the serialized
  format is basically just a list of NNAPI calls to make, so most of the
  code is spent on bounds checking.

A few notes on the design.
- Currently, all tensor sizes need to be fixed, and those fixed sizes
  are burned directly into the serialized model.  This will probably
  need to change.  NNAPI supports variable-sized tensors, but the
  important hardware backends do not.  However, we're seeing use cases
  crop up where the input size is not known until around the time that
  the model is loaded (for example, it might depend on the camera aspect
  ratio).  I think the proper fix here is to remove the code in the
  converter that eagerly calculates the sizes of the intermediate
  tensors and replace it with a code generator that will generate some
  TorchScript code that will perform those calculations at model load
  time.  This way, we will be able to support models that have
  variable-sized inputs while still only showing fixed-sized operands to
  NNAPI.
- The important hardware backends want operands to be in NHWC order, but
  PyTorch natively represents all tensors and NCHW.  The strategy for
  this is to keep NCHW during most of the conversion process, but track
  and additional value per operand representing the "dimension order".
  The dimension order gets propagated through convolutions and pointwise
  ops.  When we're ready to serialize the model, we reorder the
  dimensions for "channels last" operands to NHWC.

Test Plan:
Some local testing with FB prod models.  I'll need to add some examples
and automated tests.

Reviewed By: iseeyuan

Differential Revision: D24574040

Pulled By: dreiss

fbshipit-source-id: 6adc8571b234877ee3666ec0c0de24da35c38a1f
 2020-11-05 21:31:01 -08:00